New neural network architectures for blind music source separation have been continuously proposed as well. For example, Manilow et al. [1]} intellectually utilized the inherent synergy between transcription and source separation to improve both tasks using a multi-task learning architecture. Liu and Yang [2]} combined dilated convolution with modified gated recurrent units (GRU) to extend the receptive field of each dilated GRU unit, enabling their model to perform better and faster than state-of-the-art models for separating vocals and accompaniment.
